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 Online Journal of Bioinformatics  

 Volume 12(1):1-8, 2011

Support vector machine classification and prediction of lyases


Lavanya Rishishwar1*, Neha Mishra1, Bhasker Pant1, Kumud Pant1, Dr. K. R. Pardasani2


Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, India




Lavanya Rishishwar L, Mishra N, Pant B, Pant K, Pardasani KR, Support Vector Machine r classification and prediction of lyases, Onl J Bioinform., 12(1):1-9, 2011. A method for functionally characterizing a novel enzyme by the application of suppo rt vector machines is described. Optimal accuracy gained by this self consistency test is 91.42% with Mathew's Correlation Coefficient (MCC) of 0.57. The method was further validated by three different types of testing. The resulting accuracy for the LOO estimate was found to be 90.48% with MCC of 0.59 suggesting that data was not over fit.


Keywords: Lyases; Amino Acid Composition; Support Vector Machine; RBF kernel; Polynomial kernel; GRID.